Digital images and related image processing have created a profound impact in all aspects of modern society. From blockbuster movies to the classroom, from business presentations to the daily weather report, digital images affect and influence people—dozens, perhaps, hundreds of times per day. For example, with the advent of communications technologies such as the Internet, business persons, students, researchers and ordinary citizens routinely transmit and receive digital images in the normal course of daily activity. Thus, since digital images have become a staple in modern society, ever changing and more sophisticated requirements for image processing are consistently challenging systems designers and architects.
One such challenge relating to image processing is associated with seamlessly combining portions of a first image with portions of a second image. For example, when observing the nightly weather report, the image of the weather person is often interposed with a plurality of background screens depicting various weather patterns. In the case of high-tech movie special effects and/or other computer generated images, an object, person and/or scene portion, known as the foreground, is often digitally extracted from an associated background scene and placed into a second scene having entirely different backgrounds from the first scene. In order to accomplish this type of image extraction and migration however, it is important that remnants or artifacts of the first scene do not appear in the second scene, and that the extracted object, person or scene portion seamlessly migrate into the second scene. In other words, the migrated foreground image should appear without rough edges and appear as though the new background scene was the original background. Unfortunately, conventional image processing systems many times do not effectively achieve these goals.
One such conventional model for extracting foreground image regions from an associated background region relates to utilizing an artificial/engineered and/or clean background (e.g., blue screen), and extracting the foreground image from the artificial background via a background subtraction technique. This may be achieved by processing image pixels and determining whether a threshold level has been attained after subtracting known background pixel values from each pixel value in the image. For example, according to the conventional model, given the known background pixel values of the artificial background, the known background pixel value is subtracted from each pixel in the image and compared to a predetermined threshold. If the result of the subtraction is below the predetermined threshold, the pixel is assumed to be a background pixel and thus not assigned a value in the extracted image. If the result is above the predetermined threshold, the pixel is assumed to be a foreground pixel and thus retains its original value. Unfortunately, background subtraction and other extraction techniques may not cleanly separate foreground regions of the image and enable smooth placement of the foreground onto a new background. Some of the problems associated with these techniques relate to “bluescreen” reflections in the extracted foreground, potential “holes” in the foreground, wherein the values of the foreground and background are mixed, jagged edges along the contours of the extracted image, and “bleeding” of the previous background into the new background. Moreover, providing artificial clean backgrounds is often not possible and/or difficult to achieve.
In view of the above problems associated with conventional image processing systems, there is a need for a system and/or methodology to facilitate precise extraction of a foreground region of an image from an associated background region, and to enable smooth migration of the foreground region to other background regions.